Bayesian Nonparametric Models for External Information Borrowing Adjusting for Unmeasured Confounders

Music City Center 

We consider two classes of nonparametric Bayesian models utilizing external data for the design or analysis of an ongoing clinical trial. The first class is build on a sequence of dependent random distributions called Shared Atoms Model (SAM) that induces shared and nested clusters for covariates and outcomes. Applying to clinical trials borrowing external information (e.g., RWE) to augment the control arm, SAM attempts to match subpopulations based on measured and unmeasured confounders. The second class of Bayesian models is based on integration of Bayesian additive regression trees (BART) and meta-analytic-predictive (MAP) prior. Using the two models, the new MAP-BART model is able to accommodate potential bias caused by both measured and unmeasured confounders when external data are borrowed to form a hybrid control and infer the treatment effect of a clinical trial. We will demonstrate the methodology using numerical examples.

Real world data

Unmeasured Confounders

Information Borrow

Cluster

Match